Summary GPU First - Execution of Legacy CPU Codes on GPUs arxiv.org
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The "GPU First" compilation scheme enables running CPU codes on GPUs without changing source code, facilitating acceleration identification and quick code modification testing.
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Key Points
- The "GPU First" compilation scheme allows for the execution of legacy CPU codes on GPUs without modifying the source code.
- The scheme simplifies the identification of code regions suitable for acceleration and enables rapid testing of code modifications on actual GPUs.
- Transparent porting of legacy CPU codes to GPUs is possible and GPU performance exploration is feasible for non-experts.
- The methodology described in the document focuses on the execution of legacy CPU codes on GPUs without modifying the source code.
- The document discusses the replacement of variadic library calls with RPC calls on the device for offloading computations to the GPU.
- The GPU First methodology faces technical challenges, such as limitations in memory access and the need for annotated library headers.
- Benchmarks show that GPUs perform well for relax kernel measurements and propagation step measurements in certain benchmarks.
- The "GPU First" compilation scheme simplifies the identification of code regions that can benefit from acceleration and facilitates rapid testing of code modifications.
Summaries
26 word summary
The "GPU First" compilation scheme allows for executing legacy CPU codes on GPUs without modifying source code, simplifying acceleration identification and enabling rapid code modification testing.
46 word summary
The paper introduces a compilation scheme called "GPU First" that allows for the execution of legacy CPU codes on GPUs without modifying the source code. The scheme simplifies the task of identifying code regions suitable for acceleration and enables rapid testing of code modifications on actual
529 word summary
The paper introduces a compilation scheme called "GPU First" that allows for the execution of legacy CPU codes on GPUs without modifying the source code. The scheme simplifies the task of identifying code regions suitable for acceleration and enables rapid testing of code modifications on actual
Transparent porting of legacy CPU codes to GPUs is possible and GPU performance exploration is feasible for non-experts. By using the GPU First methodology, parallel loops can achieve performance similar to manually offloaded kernels, with up to a 14.36x
The methodology described in this document focuses on the execution of legacy CPU codes on GPUs. The authors propose a compilation and execution path that allows the user application to be compiled for the GPU without modifying the source code. They also introduce automatic generation of RPC calls
The text discusses the replacement of the variadic library call to fscanf with an RPC call on the device. The RPC call is invoked by a wrapper on the host and unpacks the arguments passed from the device. The device code is divided into call site
This text excerpt discusses the execution of legacy CPU codes on GPUs. It describes how different types of arguments are handled when offloading computations to the GPU. The first type of argument is a value that can be directly passed to the GPU. The second type
The text excerpt discusses the execution of legacy CPU codes on GPUs. It mentions the use of multiple teams in parallel kernels and the need for configurable custom allocators. The reasons for this include variations in GPU heap allocation support among vendors and the need to track
The GPU First methodology has potential but faces technical challenges. One challenge is the inability to move more than one level of memory when accessing objects through indirection, which can result in accessing device memory instead of host memory. Annotated library headers could help overcome
This summary provides a concise version of the text excerpt, highlighting key points and preserving important details.
The experiments were conducted using CUDA 11.8.0 and benchmarks were compiled with the -O3 optimization flag. The prototype version used in the
The excerpt discusses the performance of various benchmarks when executed on GPUs using the GPU First scheme. The AMGmk and page-rank benchmarks show that GPUs perform well for relax kernel measurements and propagation step measurements, respectively. However, the SPEC OMP benchmarks
The "GPU First" compilation scheme allows for the automatic compilation of legacy CPU applications directly for GPUs without the need for modification to the application source. This approach simplifies the identification of code regions that can benefit from acceleration and facilitates rapid testing of code modifications
This excerpt contains a list of references to various research papers and benchmarks related to the execution of legacy CPU codes on GPUs. Some of the key points include the co-designing of an OpenMP GPU runtime and optimizations for near-zero overhead execution, the efficient
This excerpt includes a list of references to various research papers and conference proceedings related to GPU computing, OpenMP, performance optimization, and parallel programming. The papers cover topics such as executing legacy CPU codes on GPUs, GPU-centric communication on NVIDIA GPU clusters,